hist(lipid$fdata$qc_cv, main = "The distribution of CVs")sum(lipid$fdata$qc_cv>30)[1] 13
Analysis based on the targeted lipidomic results. File: mx733759_Agus_Lipids_Single-point quant_Submit_CS_08-14-23.xlsx. The values were presented in ng/mL.
The CVs of pooled samples for each lipid species were calculated using the following formula:
\[ CV_{lipid\ species} = SD_{lipid\ species}/Mean_{lipid\ species} * 100\% \]
We are less confident with the lipid species with a high CV. These lipid species will be excluded for further analysis.
hist(lipid$fdata$qc_cv, main = "The distribution of CVs")sum(lipid$fdata$qc_cv>30)[1] 13
We excluded lipid species with a CV > 30%. 17 features were excluded.
For some lipid species, the mean concentration in HDL samples was even lower than the mean of blank samples. We felt less confident with the results of these lipid species and excluded them from further analysis.
sum(lipid$fdata$sample_mean < lipid$fdata$blank_mean)[1] 0
13 species were excluded.
lipid <- subset_features(lipid, lipid$fdata$qc_cv<30)The hierarchical relationship of lipid classes.
# edata
lipid.class <- summarize_feature(lipid, "Categories")
edata.prop <- apply(lipid.class$edata, 2, function(col){
col/sum(col)*100
})
# pie chart
pies.class1 <- lapply(sampleNames(lipid), plotPie, edata.prop)
legend.class1 <- get_legend(pies.class1[[1]])
pies.class1 <- lapply(pies.class1, function(x)x+theme(legend.position = "none"))
plot_grid(plotlist = pies.class1, nrow = 3) # bar plot
plotBar(edata.prop)edata.scaled <- scale(t(edata.prop), center = T)
res.pca <- PCA(edata.scaled, scale.unit = T, graph = FALSE)
# fviz_eig(res.pca, addlabels = TRUE)
fviz_pca_ind(res.pca,
axes = c(1,2),
repel = TRUE,
title = NULL
) +
theme_cynthia_bw() +
theme(
legend.position = "none"
) +
labs(title = NULL)lipid.class2 <- summarize_feature(lipid, "Main.class")
edata.prop2 <- apply(lipid.class2$edata, 2, function(col){
col/sum(col)*100
})
# pie chart
pies.class2 <- lapply(sampleNames(lipid), plotPie, edata.prop2)
legend.class2 <- get_legend(pies.class2[[1]])
pies.class2 <- lapply(pies.class2, function(x)x+theme(legend.position = "none"))
plot_grid(plotlist = pies.class2, nrow = 3)# bar plot
plotBar(edata.prop2)edata.scaled2 <- scale(t(edata.prop2), center = T)
res.pca <- PCA(edata.scaled2, scale.unit = T, graph = FALSE)
# fviz_eig(res.pca, addlabels = TRUE)
fviz_pca_ind(res.pca,
axes = c(1,2),
repel = TRUE,
title = NULL
) +
theme_cynthia_bw() +
theme(
legend.position = "none"
) +
labs(title = NULL)lipid.class3 <- summarize_feature(lipid, "class")
edata.prop3 <- apply(lipid.class3$edata, 2, function(col){
col/sum(col)*100
})
# pie chart
pies.class3 <- lapply(sampleNames(lipid), plotPie, edata.prop3)
legend.class3 <- get_legend(pies.class3[[1]]+theme(legend.position = "bottom"))
pies.class3 <- lapply(pies.class3, function(x)x+theme(legend.position = "none"))
plot_grid(plotlist = pies.class3, nrow = 3)# bar plot
plotBar(edata.prop3)edata.scaled3 <- scale(t(edata.prop3), center = T)
res.pca <- PCA(edata.scaled3, scale.unit = T, graph = FALSE)
# fviz_eig(res.pca, addlabels = TRUE)
fviz_pca_ind(res.pca,
axes = c(1,2),
repel = TRUE,
title = NULL
) +
theme_cynthia_bw() +
theme(
legend.position = "none"
) +
labs(title = NULL)